絞り込み結果
98 件並び順
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年9月28日ポッドキャスト · 2 人DNA Is Code, but You Compile It with a Pipetteホスト Sarah Guo, Elad GilGinkgo Bioworks CEO Jason Kelly tells No Priors why cells are worth the magic, why biology has no AWS, and why a pandemic should look like cybersecurity.
46:37EN
20VC with Harry Stebbings· 2023年9月22日ポッドキャスト · 2 人Christian Kleinerman on AI Models, Enterprise Data and Product Decisionsゲスト Christian Kleinerman · ホスト Harry StebbingsSnowflake’s product leader discusses model choice, enterprise adoption and the trade-offs between speed, reliability and simplicity.
2:15:11EN
Cognitive Revolution "How AI Changes Everything"· 2023年9月22日ポッドキャストTwenty Gibberish Tokens Jailbroke ChatGPT and Bardホスト Nathan LabenzCarnegie Mellon’s Zico Kolter, Andy Zou, and Asher Trockman walk through a universal jailbreak that transfers from open models to commercial APIs, and a training-free way to steal some of pretraining’s benefit.
9:59EN
Matthew Berman· 2023年9月7日Open Interpreter: A 2023 Local Code-Execution WalkthroughMatthew Berman · Forward FutureMatthew Berman installs Open Interpreter, analyzes local files with GPT-4 and tries a local Code Llama setup, with mixed results.
4:48ENMatthew Berman· 2023年7月12日PoisonGPT: A Demonstration of Model Supply-Chain RiskMatthew Berman · Forward FutureMatthew Berman discusses Mithril Security’s 2023 demonstration of an edited GPT-J model and a misleading repository name. The experiment illustrates a supply-chain risk, not a breach of the original model provider.
54:00EN20VC with Harry Stebbings· 2023年6月30日ポッドキャスト · 2 人Douwe Kiela on Data, Retrieval and Enterprise AIゲスト Douwe Kiela · ホスト Harry StebbingsThe Contextual AI CEO discusses language model trade-offs, evaluation, open models and the hurdles to enterprise use.
51:22ENNo Priors: AI, Machine Learning, Tech, & Startups· 2023年6月29日ポッドキャスト · 2 人Three IPOs Later, Frank Slootman Still Thinks the Job Is Urgencyホスト Sarah Guo, Elad GilSnowflake's CEO tells No Priors how he picked the right elevator, turned a 15-person backup startup into an EMC acquisition, and why a data lake is a landfill you should not train on.
6:49ENMatthew Berman· 2023年6月21日Reddit’s API Changes, AI Training Data and the Open InternetMatthew Berman · Forward FutureMatthew Berman argues that the 2023 Reddit API dispute exposed tensions between AI data demand, third-party developers and the people who create online content.
3:15:07EN
Lex Fridman· 2023年6月18日ポッドキャスト · 2 人Wikipedia Worked Once Writing Stopped Feeling Like Grad Schoolゲスト Jimmy Wales · ホスト Lex FridmanJimmy Wales tells Lex Fridman how Nupedia's seven-stage review died, why Wikipedia still refuses ads and governments, and what large language models should actually do with an encyclopedia.
59:28ENCognitive Revolution "How AI Changes Everything"· 2023年6月16日ポッドキャストFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Productホスト Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
2:00:19ENCognitive Revolution "How AI Changes Everything"· 2023年6月6日ポッドキャスト1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2ホスト Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…
10:16ENMatthew Berman· 2023年5月21日DarkBERT: Adapting a Language Model to Dark-Web TextMatthew Berman · Forward FutureMatthew Berman reviews research on a RoBERTa-based model and its cybersecurity evaluations.
1:13:52ENMatthew Berman· 2023年4月15日Andriy Mulyar on GPT4All, Data Quality and Open Local ModelsMatthew Berman, Andriy Mulyar · Forward FutureThe Nomic AI co-founder explains how GPT4All emerged, why training data matters, and the tradeoffs between local and centralized AI.
8:02ENMatthew Berman· 2023年4月12日Meta’s Segment Anything: Promptable Image Masks and the SA-1B DatasetMatthew Berman · Forward FutureIn April 2023, Matthew Berman reviews Meta’s Segment Anything project and demonstrates selecting image regions with clicks, boxes and automatic mask generation. The model segments pixels; it does not by itself name every object or guarantee accurate results on every image.
6:24ENMatthew Berman· 2023年4月6日BloombergGPT: Mixing Financial and General Data for a Language ModelMatthew Berman · Forward FutureMatthew Berman examines the 2023 finance-focused research model, its training corpus, evaluation and decision not to release weights.
5:08ENMatthew Berman· 2023年4月3日Chatting with a PDF in Colab Using LangChain and a Vector IndexMatthew Berman · Forward FutureMatthew Berman demonstrates a 2023 PDF question-answering notebook; its retrieval workflow does not fine-tune model weights and its revenue example is incorrect.
1:29:31ENCognitive Revolution "How AI Changes Everything"· 2023年3月30日ポッドキャスト130,000 Teleop Demos: Google's GPT Playbook for Robotsホスト Nathan LabenzKeerthana Gopalakrishnan of Google Robotics tells Nathan Labenz how RT-1, SayCan, and PaLM-E turn language-model scaling into kitchen-ready mobile manipulators.
1:03:29ZH
跟李沐学AI· 2023年1月12日多模态论文串讲：BLIP、CoCa 与 BEiT-3从图文数据清理到统一预训练，梳理三种视觉语言模型的结构、目标函数与相互联系。
2:31:48ENLex Fridman· 2021年12月28日ポッドキャスト · 2 人A Car That Cannot Drive Itself Will Be About as Useful as a Horseゲスト Elon Musk · ホスト Lex FridmanIn a Lex Fridman conversation built around Tesla Autopilot, Elon Musk argues that every driver input is an error, that today’s hardware is already enough for full self-driving, and that watching the human will soon matter less than making the system safer than a person.
1:28:46ENLex Fridman· 2021年1月4日ポッドキャスト · 2 人The Speech Company That Gets Paid to Label Its Own Training Dataゲスト Dan Kokotov · ホスト Lex FridmanRev VP of engineering Dan Kokotov tells Lex Fridman why a $1.25-a-minute human transcript, a 14% machine word-error rate, and a two-sided freelancer market are the same flywheel.
1:12:03ENLex Fridman· 2020年5月5日ポッドキャスト · 2 人Mice Don't Get Alzheimer's: Daphne Koller on Disease in a Dishゲスト Daphne Koller · ホスト Lex FridmanThe Coursera co-founder and insitro CEO on why most diseases are not one disease, how to turn cells into data, and why overconfident neural nets can kill.
1:17:29ENLex Fridman· 2019年9月23日ポッドキャスト · 2 人The Algorithm Is Already Better. The Clinic Still Uses 1967.ゲスト Regina Barzilay · ホスト Lex FridmanMIT's Regina Barzilay on why deep learning already beats the breast-cancer 'density' score — and why data, liability, and incentives still will not move.
57:44ENLex Fridman· 2019年8月1日ポッドキャスト · 2 人Kevin Scott: Treat AI Like a Hash Table, and Measure Platforms by What Others Earnゲスト Kevin Scott · ホスト Lex FridmanMicrosoft's CTO talks radical markets, data dignity, Dunbar's number, and why a platform fails if only a handful of companies decide what AI is for.
32:45ENLex Fridman· 2019年4月12日ポッドキャスト · 2 人Elon Musk: Every Driver Input Is an Error Tesla Autopilot Must Eliminateゲスト Elon Musk · ホスト Lex FridmanAfter MIT published a study of functional vigilance on Autopilot, Lex Fridman sat down with Musk to argue about cameras on drivers, fleet data, and whether humans still belong in the loop.もっと見る98 件中 24 件を表示